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Distributed Compression for MIMO Coordinated Networks with a Backhaul Constraint
"... Abstract—We consider the uplink of a backhaulconstrained, MIMO coordinated network. That is, a singlefrequency network with ..."
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Abstract—We consider the uplink of a backhaulconstrained, MIMO coordinated network. That is, a singlefrequency network with
Crosslayer optimization for MIMObased wireless ad hoc netoworks: Routing, power allocation, and bandwidth allocation
 IEEE Journal on Selected Areas in Communications
, 2008
"... Abstract—MIMObased communications systems have great potential to improve network capacity for wireless ad hoc networks. Due to unique physical layer characteristics associated with MIMO, network performance is tightly coupled with mechanisms at physical, link, and routing layers. So far, research ..."
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Abstract—MIMObased communications systems have great potential to improve network capacity for wireless ad hoc networks. Due to unique physical layer characteristics associated with MIMO, network performance is tightly coupled with mechanisms at physical, link, and routing layers. So far, research on MIMObased wireless ad hoc networks is still in its infancy and few results are available. In this paper, we consider the problem of jointly optimizing power and bandwidth allocation at each node and multihop/multipath routing in a MIMObased wireless ad hoc network. We develop a solution procedure to this crosslayer optimization problem and use simulations to validate the efficacy of this solution. Index Terms—Multipleinput multipleoutput (MIMO), multihop ad hoc network, crosslayer optimization. I.
Weighted proportional fairness capacity for Gaussian MIMO broadcast channels
, 2007
"... Abstract—Recently, there has been tremendous interest in exploring the capacity region of multipleinput multipleoutput broadcast channels (MIMOBC). However, fairness, a very important performance measure of multiuser communications systems and networks, has not been addressed for MIMOBC in the ..."
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Abstract—Recently, there has been tremendous interest in exploring the capacity region of multipleinput multipleoutput broadcast channels (MIMOBC). However, fairness, a very important performance measure of multiuser communications systems and networks, has not been addressed for MIMOBC in the literature. In this paper, we study how to determine the weighted proportional fairness (WPF) capacity of MIMOBC. The difficulty of finding the WPF capacity of MIMOBC lies in that it contains two difficult subproblems: 1) a complex combinatorial optimization problem to determine the optimal decoding order in the dual MIMO multiple access channel (MIMOMAC) and 2) a nonconvex optimization problem in computing the optimal input covariance matrices to achieve WPF capacity. To circumvent the difficulty in the first subproblem, we derive a set of optimality conditions that the optimal decoding order must satisfy. Based on these optimality conditions, we design an efficient algorithm called iterative gradient sorting (IGS) to determine the optimal decoding order by iteratively sorting the gradient entries and moving across corner points. We also show that this method can be geometrically interpreted as sequential gradient projections. For the second subproblem, we propose an efficient algorithm based on conjugate gradient projection (CGP) technique, which employs the concept of Hessian conjugate. We also develop a polynomial time algorithm to solve the projection subproblem. I.
Maximum weighted sum rate of multiantenna broadcast channels,” 2007
 Online]. Available: http://arXiv:cs/0703111v1. April
"... Abstract — Recently, researchers showed that dirty paper coding (DPC) is the optimal transmission strategy for multipleinput multipleoutput broadcast channels (MIMOBC). In this paper, we study how to determine the maximum weighted sum of DPC rates through solving the maximum weighted sum rate pro ..."
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Abstract — Recently, researchers showed that dirty paper coding (DPC) is the optimal transmission strategy for multipleinput multipleoutput broadcast channels (MIMOBC). In this paper, we study how to determine the maximum weighted sum of DPC rates through solving the maximum weighted sum rate problem of the dual MIMO multiple access channel (MIMOMAC) with a sum power constraint. We first simplify the maximum weighted sum rate problem such that enumerating all possible decoding orders in the dual MIMOMAC is unnecessary. We then design an efficient algorithm based on conjugate gradient projection (CGP) to solve the maximum weighted sum rate problem. Our proposed CGP method utilizes the powerful concept of Hessian conjugacy. We also develop a rigorous algorithm to solve the projection problem. We show that CGP enjoys provable convergence, nice scalability, and great efficiency for large MIMOBC systems. I.
1 Capacity Bounds for Gaussian MIMO relay channel with Channel State Information
"... Abstract—In this paper, source and relay precoders are derived which optimize upper and lower bounds on the Gaussian MIMO relay channel capacity. First, the prior art on the cutset upperbound on capacity is extended by showing that the optimization of the source and relay codebooks can be formulate ..."
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Abstract—In this paper, source and relay precoders are derived which optimize upper and lower bounds on the Gaussian MIMO relay channel capacity. First, the prior art on the cutset upperbound on capacity is extended by showing that the optimization of the source and relay codebooks can be formulated as a convex problem without having to introduce a scalar parameter that captures their crosscorrelation. Both the FullDuplex and Time Division Duplex (TDD) relay channels are addressed, assuming perfect knowledge of all channels, and two procedures are proposed which solve the problem efficiently by relying on analytical expressions of gradients, subgradients and projection operators: the first one solves the dual problem while the second one applies the barrier method. Similar techniques are then used to maximize the achievable rate of DecodeandForward (DF) TDD MIMO relaying strategies with either partial or full decoding at the relay. Suboptimum precoders are also proposed which have a closedform expression that can be obtained from the KKT conditions, thus reducing the computational complexity at the expense of a lower rate. Simulations in a cellular downlink scenario show that the partial DF strategy can achieve a rate very close to capacity for realistic values of the Source to Relay signaltonoise ratio. Finally, the availability of Channel State Information (CSI) in a real system is discussed.
Weighted Sum Rate Maximization for the MIMODownlink Using a Projected Conjugate Gradient Algorithm
"... Abstract — The maximization of a weighted sum of data rates is an essential point in crosslayer based resource allocation. Several algorithms have been proposed in the literature to solve this problem for the downlink of a multiple antenna system employing dirty paper precoding at the base station. ..."
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Abstract — The maximization of a weighted sum of data rates is an essential point in crosslayer based resource allocation. Several algorithms have been proposed in the literature to solve this problem for the downlink of a multiple antenna system employing dirty paper precoding at the base station. However, they all suffer from a relatively slow convergence if the true number of objective function evaluations is taken into account. In this paper, an improved conjugate gradient method is presented, that takes the power constraint into account in the calculation of the search direction. Its superior convergence properties compared to existing approaches are verified by MonteCarlo simulations for various scenarios. I.
On the maximum weighted sumrate of MIMO Gaussian broadcast channels
 in Proc. IEEE ICC
, 2008
"... Abstract—In this paper, we investigate the maximum weighted sumrate problem (MWSR) of MIMO Gaussian broadcast channels (MIMOBC). We propose an efficient algorithm that employs conjugate gradient projections (CGP) to solve the MWSR problem. The proposed CGP offers provable convergence. By deflect ..."
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Abstract—In this paper, we investigate the maximum weighted sumrate problem (MWSR) of MIMO Gaussian broadcast channels (MIMOBC). We propose an efficient algorithm that employs conjugate gradient projections (CGP) to solve the MWSR problem. The proposed CGP offers provable convergence. By deflecting gradient direction to its Hessian conjugate, CGP enjoys a superlinear convergence rate. Also, CGP has a modest memory requirement. It only needs the solution information from the previous step. More importantly, CGP is able to solve the MWSR problem with arbitrary number of antennas on both sides of a MIMOBC. I.
Weighted Sum Rate Maximization for MIMOOFDM Systems with Linear and Dirty Paper Precoding
"... Many sophisticated resource allocation strategies are based on the maximization of the weighted sum of data rates for a given transmit power. While this problem can be easily solved for orthogonal multiple access schemes like TDMA, it is much more complicated if users are separated in space using mu ..."
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Many sophisticated resource allocation strategies are based on the maximization of the weighted sum of data rates for a given transmit power. While this problem can be easily solved for orthogonal multiple access schemes like TDMA, it is much more complicated if users are separated in space using multiple antennas at the base station due to the mutual coupling. In this paper, we propose a new projected conjugate gradient algorithm for the optimization of the transmit filters. The power constraint is taken into account in the calculation of the search direction by projecting the gradient onto a tangent hyperplane. Our method features excellent convergence properties when applied to dirty paper precoding, and it may also be used for the optimization of linear precoders. 1
1 Distributed Compression for the Uplink of a BackhaulConstrained Coordinated Cellular Network
, 2008
"... We consider a backhaulconstrained coordinated cellular network. That is, a singlefrequency network with N+1 multiantenna base stations (BSs) that cooperate in order to decode the users ’ data, and that are linked by means of a common lossless backhaul, of limited capacity R. To implement receive ..."
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We consider a backhaulconstrained coordinated cellular network. That is, a singlefrequency network with N+1 multiantenna base stations (BSs) that cooperate in order to decode the users ’ data, and that are linked by means of a common lossless backhaul, of limited capacity R. To implement receive cooperation, we propose distributed compression: N BSs, upon receiving their signals, compress them using a multisource lossy compression code. Then, they send the compressed vectors to a central BS, which performs users ’ decoding. Distributed WynerZiv coding is proposed to be used, and is optimally designed in this work. The first part of the paper is devoted to a network with a unique multiantenna user, that transmits a predefined Gaussian spacetime codeword. For such a scenario, the compression codebooks at the BSs are optimized, considering the user’s achievable rate as the performance metric. In particular, for N = 1 the optimum codebook distribution is derived in closed form, while for N> 1 an iterative algorithm is devised. The second part of the contribution focusses on the multiuser scenario. For it, the achievable rate region is obtained by means of the optimum compression codebooks for sumrate and weighted sumrate, respectively.